Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 1066 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 1053 | - |
dc.citation.title | Korean Journal of Remote Sensing | - |
dc.citation.volume | 36 | - |
dc.contributor.author | Choi, Hyunyoung | - |
dc.contributor.author | Kang, Yoojin | - |
dc.contributor.author | Im, Jungho | - |
dc.contributor.author | Park, Seohui | - |
dc.contributor.author | Shin, Minso | - |
dc.contributor.author | Kim, Sang-Min | - |
dc.date.accessioned | 2023-12-21T16:47:19Z | - |
dc.date.available | 2023-12-21T16:47:19Z | - |
dc.date.created | 2021-01-08 | - |
dc.date.issued | 2020-10 | - |
dc.description.abstract | Sulfur dioxide (SO2) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO2 can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO2 crucial. In South Korea, SO2 monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO2 concentrations. Thus, this research estimated spatially continuous ground-level SO2 concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO2 estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO2 concentrations based on the proposed model agreed with the general seasonality of SO2 and the temporal patterns of emission sources. | - |
dc.identifier.bibliographicCitation | Korean Journal of Remote Sensing, v.36, no.5, pp.1053 - 1066 | - |
dc.identifier.doi | 10.7780/kjrs.2020.36.5.3.5 | - |
dc.identifier.issn | 1225-6161 | - |
dc.identifier.scopusid | 2-s2.0-85106462527 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/49504 | - |
dc.language | 한국어 | - |
dc.publisher | 대한원격탐사학회 | - |
dc.title.alternative | 위성 자료와 수치모델 자료를 활용한 스태킹 앙상블 기반 SO2 지상농도 추정 | - |
dc.title | Monitoring Ground-level SO2 Concentrations Based on a Stacking Ensemble Approach Using Satellite Data and Numerical Models | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.identifier.kciid | ART002643760 | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
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